What is the Point of a Modern Data Architecture?
Good question. What is the point? The point is to create measurable business value from enterprise data. Of course, before measurable business value comes insight. The Modern Data Architecture (MDA) recognizes that insight can lie hidden in data of all types—structured or unstructured, messy or modeled, historical or realtime.
An MDA combined with a semantic data catalog supports an efficient data supply chain. An MDA should remain flexible because the data sources it must accommodate will inevitably change. Right now, a common misperception about modern data architectures—yes, plural because no two are alike—is that it simply means legacy data warehouses plus Hadoop.
As we frequently point out, a modern data architecture stores data as is. It handles the volume, velocity, and variety of big data without premodeling. Most organizations have integrated Hadoop and NoSQL as part of a hybrid architecture. But that doesn’t describe the only form an MDA can take.
It’s because the data sources supported by an MDA will change that a semantic data catalog is so important. The data catalog is a practical expression of the data architecture at any point in time. If a component is added to the architecture, such as a complex event processing (CEP) engine, the catalog will reflect that as it periodically updates.
Within the context of a modern data architecture, the data catalog helps organizations meet the challenges that Accenture describes as “accelerating data movement, processing, and interactivity—[to enable] decision makers to more swiftly capture and act on insights from their data as well as achieve returns on their analytics investments.”
For a deeper dive on the modern data architecture and semantic data catalog, see our white paper: A Semantic Data Catalog Drives Agility in the Modern Data Architecture.
To hear Brian Hopkins, VP and Principal Analyst and Forrester Research, talk about an insights-driven data architecture, register for our Jan 31 webinar, How Hadoop Drives Agility across the Data Stack.